OpenRaiser/Intern-Atlas huggingface dataset:
Methods for Actors in the Electric Power System to Prevent, Detect and React to ICT Attacks and Failures
Venue: arXiv cs.CR 2020
Year: 2020

The fundamental changes in power supply and increasing decentralization require more active grid operation and an increased integration of ICT at all power system actors. This trend raises complexity and increasingly leads to interactions between primary grid operation and ICT as well as different power system actors. For example, virtual power plants control various assets in the distribution grid via ICT to jointly market existing flexibilities. Failures of ICT or targeted attacks can thus have serious effects on security of supply and system stability. This paper presents a holistic approach to providing methods specifically for actors in the power system for prevention, detection, and reaction to ICT attacks and failures. The focus of our measures are solutions for ICT monitoring, systems for the detection of ICT attacks and intrusions in the process network, and the provision of actionable guidelines as well as a practice environment for the response to potential ICT security incidents.
Developing a Personality Model for Speech-based Conversational Agents Using the Psycholexical Approach
Venue: arXiv cs.HC 2020
Year: 2020

We present the first systematic analysis of personality dimensions developed specifically to describe the personality of speech-based conversational agents. Following the psycholexical approach from psychology, we first report on a new multi-method approach to collect potentially descriptive adjectives from 1) a free description task in an online survey (228 unique descriptors), 2) an interaction task in the lab (176 unique descriptors), and 3) a text analysis of 30,000 online reviews of conversational agents (Alexa, Google Assistant, Cortana) (383 unique descriptors). We aggregate the results into a set of 349 adjectives, which are then rated by 744 people in an online survey. A factor analysis reveals that the commonly used Big Five model for human personality does not adequately describe agent personality. As an initial step to developing a personality model, we propose alternative dimensions and discuss implications for the design of agent personalities, personality-aware personalisation, and future research.
Experimental Evaluation of a Method to Simplify Expressions
Venue: arXiv cs.LO 2020
Year: 2020

We present a method to simplify expressions in the context of an equational theory. The basic ideas and concepts of the method have been presented previously elsewhere but here we tackle the difficult task of making it efficient in practice, in spite of its great generality. We first recall the notion of a collection of structures, which allows us to manipulate very large (possibly infinite) sets of terms as a whole, i.e., without enumerating their elements. Then we use this tool to construct algorithms to simplify expressions. We give various reasons why it is difficult to make these algorithms precise and efficient. We then propose a number of approches to solve the raised issues. Finally, and importantly, we provide a detailed experimental evaluation of the method and a comparison of several variants of it. Although the method is completely generic, we use (arbitrary, not only two-level) boolean expressions as the application field for these experiments because impressive simplifications can be obtained in spite of the hardness of the problem.
Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework using Visual and Depth Cues
Venue: arXiv cs.CV 2020
Year: 2020

This paper addresses the problem of building augmented metric representations of scenes with semantic information from RGB-D images. We propose a complete framework to create an enhanced map representation of the environment with object-level information to be used in several applications such as human-robot interaction, assistive robotics, visual navigation, or in manipulation tasks. Our formulation leverages a CNN-based object detector (Yolo) with a 3D model-based segmentation technique to perform instance semantic segmentation, and to localize, identify, and track different classes of objects in the scene. The tracking and positioning of semantic classes is done with a dictionary of Kalman filters in order to combine sensor measurements over time and then providing more accurate maps. The formulation is designed to identify and to disregard dynamic objects in order to obtain a medium-term invariant map representation. The proposed method was evaluated with collected and publicly available RGB-D data sequences acquired in different indoor scenes. Experimental results show the potential of the technique to produce augmented semantic maps containing several objects (notably doors). We also provide to the community a dataset composed of annotated object classes (doors, fire extinguishers, benches, water fountains) and their positioning, as well as the source code as ROS packages.
Automating Botnet Detection with Graph Neural Networks
Venue: arXiv cs.CR 2020
Year: 2020

Botnets are now a major source for many network attacks, such as DDoS attacks and spam. However, most traditional detection methods heavily rely on heuristically designed multi-stage detection criteria. In this paper, we consider the neural network design challenges of using modern deep learning techniques to learn policies for botnet detection automatically. To generate training data, we synthesize botnet connections with different underlying communication patterns overlaid on large-scale real networks as datasets. To capture the important hierarchical structure of centralized botnets and the fast-mixing structure for decentralized botnets, we tailor graph neural networks (GNN) to detect the properties of these structures. Experimental results show that GNNs are better able to capture botnet structure than previous non-learning methods when trained with appropriate data, and that deeper GNNs are crucial for learning difficult botnet topologies. We believe our data and studies can be useful for both the network security and graph learning communities.
Towards a Resilient Machine Learning Classifier -- a Case Study of Ransomware Detection
Venue: arXiv cs.CR 2020
Year: 2020

The damage caused by crypto-ransomware, due to encryption, is difficult to revert and cause data losses. In this paper, a machine learning (ML) classifier was built to early detect ransomware (called crypto-ransomware) that uses cryptography by program behavior. If a signature-based detection was missed, a behavior-based detector can be the last line of defense to detect and contain the damages. We find that input/output activities of ransomware and the file-content entropy are unique traits to detect crypto-ransomware. A deep-learning (DL) classifier can detect ransomware with a high accuracy and a low false positive rate. We conduct an adversarial research against the models generated. We use simulated ransomware programs to launch a gray-box analysis to probe the weakness of ML classifiers and to improve model robustness. In addition to accuracy and resiliency, trustworthiness is the other key criteria for a quality detector. Making sure that the correct information was used for inference is important for a security application. The Integrated Gradient method was used to explain the deep learning model and also to reveal why false negatives evade the detection. The approaches to build and to evaluate a real-world detector were demonstrated and discussed.
Mutual Information Maximization for Effective Lip Reading
Venue: arXiv cs.CV 2020
Year: 2020

Lip reading has received an increasing research interest in recent years due to the rapid development of deep learning and its widespread potential applications. One key point to obtain good performance for the lip reading task depends heavily on how effective the representation can be to capture the lip movement information and meanwhile to resist the noises resulted from the change of pose, lighting conditions, speaker's appearance and so on. Towards this target, we propose to introduce the mutual information constraints on both the local feature's level and the global sequence's level to enhance the relations of the features with the speech content. On the one hand, we constraint the features generated at each time step to enable them carry a strong relation with the speech content by imposing the local mutual information maximization constraint (LMIM), leading to improvements over the model's ability to discover fine-grained lip movements and the fine-grained differences among words with similar pronunciation, such as ``spend'' and ``spending''. On the other hand, we introduce the mutual information maximization constraint on the global sequence's level (GMIM), to make the model be able to pay more attention to discriminate key frames related with the speech content, and less to various noises appeared in the speaking process. By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading. To verify this method, we evaluate on two large-scale benchmark. We perform a detailed analysis and comparison on several aspects, including the comparison of the LMIM and GMIM with the baseline, the visualization of the learned representation and so on. The results not only prove the effectiveness of the proposed method but also report new state-of-the-art performance on both the two benchmarks.
Patch Graph Rewriting (Extended Version)
Venue: arXiv cs.LO 2020
Year: 2020

The basic principle of graph rewriting is the stepwise replacement of subgraphs inside a host graph. A challenge in such replacement steps is the treatment of the patch graph, consisting of those edges of the host graph that touch the subgraph, but are not part of it. We introduce the patch graph rewriting framework, a visual graph rewriting language with precise formal semantics. The language has rich expressive power in two ways. Firstly, rules can flexibly constrain the permitted shapes of patches touching matching subgraphs. Secondly, rules can freely transform patches. While the framework is designed to be easy to understand, it subsumes many approaches to graph rewriting.
Leveraging Vision and Kinematics Data to Improve Realism of Biomechanic Soft-tissue Simulation for Robotic Surgery
Venue: arXiv cs.RO 2020
Year: 2020

Purpose Surgical simulations play an increasingly important role in surgeon education and developing algorithms that enable robots to perform surgical subtasks. To model anatomy, Finite Element Method (FEM) simulations have been held as the gold standard for calculating accurate soft-tissue deformation. Unfortunately, their accuracy is highly dependent on the simulation parameters, which can be difficult to obtain. Methods In this work, we investigate how live data acquired during any robotic endoscopic surgical procedure may be used to correct for inaccurate FEM simulation results. Since FEMs are calculated from initial parameters and cannot directly incorporate observations, we propose to add a correction factor that accounts for the discrepancy between simulation and observations. We train a network to predict this correction factor. Results To evaluate our method, we use an open-source da Vinci Surgical System to probe a soft-tissue phantom and replay the interaction in simulation. We train the network to correct for the difference between the predicted mesh position and the measured point cloud. This results in 15-30% improvement in the mean distance, demonstrating the effectiveness of our approach across a large range of simulation parameters. Conclusion We show a first step towards a framework that synergistically combines the benefits of model-based simulation and real-time observations. It corrects discrepancies between simulation and the scene that results from inaccurate modeling parameters. This can provide a more accurate simulation environment for surgeons and better data with which to train algorithms.
Simultaneous Downlink Data Transmission and Uplink Channel Estimation with Reduced Complexity Full Duplex MIMO Radios
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we study Full Duplex (FD) Multiple-Input Multiple-Output (MIMO) radios for simultaneous data communication and control information exchange. Capitalizing on a recently proposed FD MIMO architecture combining digital transmit and receive beamforming with reduced complexity multi-tap analog Self-Interference (SI) cancellation, we propose a novel transmission scheme exploiting channel reciprocity for joint downlink beamformed information data communication and uplink channel estimation through training data transmission. We adopt a general model for pilot-assisted channel estimation and present a unified optimization framework for all involved FD MIMO design parameters. Our representative Monte Carlo simulation results for an example algorithmic solution for the beamformers as well as for the analog and digital SI cancellation demonstrate that the proposed FD-based joint communication and control scheme provides 1.4x the downlink rate of its half duplex counterpart. This performance improvement is achieved with 50% reduction in the hardware complexity for the analog canceller than conventional FD MIMO architectures with fully connected analog cancellation.
Security Analysis of EOSIO Smart Contracts
Venue: arXiv cs.CR 2020
Year: 2020

The EOSIO blockchain, one of the representative Delegated Proof-of-Stake (DPoS) blockchain platforms, has grown rapidly recently. Meanwhile, a number of vulnerabilities and high-profile attacks against top EOSIO DApps and their smart contracts have also been discovered and observed in the wild, resulting in serious financial damages. Most of EOSIO's smart contracts are not open-sourced and they are typically compiled to WebAssembly (Wasm) bytecode, thus making it challenging to analyze and detect the presence of possible vulnerabilities. In this paper, we propose EOSAFE, the first static analysis framework that can be used to automatically detect vulnerabilities in EOSIO smart contracts at the bytecode level. Our framework includes a practical symbolic execution engine for Wasm, a customized library emulator for EOSIO smart contracts, and four heuristics-driven detectors to identify the presence of four most popular vulnerabilities in EOSIO smart contracts. Experiment results suggest that EOSAFE achieves promising results in detecting vulnerabilities, with an F1-measure of 98%. We have applied EOSAFE to all active 53,666 smart contracts in the ecosystem (as of November 15, 2019). Our results show that over 25% of the smart contracts are vulnerable. We further analyze possible exploitation attempts against these vulnerable smart contracts and identify 48 in-the-wild attacks (25 of them have been confirmed by DApp developers), resulting in financial loss of at least 1.7 million USD.
Semi-supervised Disentanglement with Independent Vector Variational Autoencoders
Venue: arXiv cs.LG 2020
Year: 2020

We aim to separate the generative factors of data into two latent vectors in a variational autoencoder. One vector captures class factors relevant to target classification tasks, while the other vector captures style factors relevant to the remaining information. To learn the discrete class features, we introduce supervision using a small amount of labeled data, which can simply yet effectively reduce the effort required for hyperparameter tuning performed in existing unsupervised methods. Furthermore, we introduce a learning objective to encourage statistical independence between the vectors. We show that (i) this vector independence term exists within the result obtained on decomposing the evidence lower bound with multiple latent vectors, and (ii) encouraging such independence along with reducing the total correlation within the vectors enhances disentanglement performance. Experiments conducted on several image datasets demonstrate that the disentanglement achieved via our method can improve classification performance and generation controllability.
Image Encryption Decryption Using Chaotic Logistic Mapping and DNA Encoding
Venue: arXiv cs.CR 2020
Year: 2020

In this paper we have proposed a method that uses chaotic logistic mapping and DNA encoding to encrypt the image. A 32 bit ASCII private key is used to diffuse the image. The results demonstrated clearly show that encryption algorithm based on chaotic logistic mapping and DNA encoding gives better result than encrypting only with chaotic logistic mapping. The proposed method also takes into account the possible parametric like Peak Signal to Noise Ratio and Structure Similarity.
Approximation Schemes for Subset Sum Ratio Problems
Venue: arXiv cs.DS 2020
Year: 2020

We consider the Subset Sum Ratio Problem ($SSR$), in which given a set of integers the goal is to find two subsets such that the ratio of their sums is as close to~1 as possible, and introduce a family of variations that capture additional meaningful requirements. Our main contribution is a generic framework that yields fully polynomial time approximation schemes (FPTAS) for problems in this family that meet certain conditions. We use our framework to design explicit FPTASs for two such problems, namely Two-Set Subset-Sum Ratio and Factor-$r$ Subset-Sum Ratio, with running time $\mathcal{O}(n^4/\varepsilon)$, which coincides with the best known running time for the original $SSR$ problem [15].
Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in large-scale optimal transport have greatly extended its application scenarios in machine learning. However, existing methods either not explicitly learn the transport map or do not support general cost function. In this paper, we propose an end-to-end approach for large-scale optimal transport, which directly solves the transport map and is compatible with general cost function. It models the transport map via stochastic neural networks and enforces the constraint on the marginal distributions via adversarial training. The proposed framework can be further extended towards learning Monge map or optimal bijection via adopting cycle-consistency constraint(s). We verify the effectiveness of the proposed method and demonstrate its superior performance against existing methods with large-scale real-world applications, including domain adaptation, image-to-image translation, and color transfer.
Tiny Eats: Eating Detection on a Microcontroller
Venue: arXiv cs.LG 2020
Year: 2020

There is a growing interest in low power highly efficient wearable devices for automatic dietary monitoring (ADM) [1]. The success of deep neural networks in audio event classification problems makes them ideal for this task. Deep neural networks are, however, not only computationally intensive and energy inefficient but also require a large amount of memory. To address these challenges, we propose a shallow gated recurrent unit (GRU) architecture suitable for resource-constrained applications. This paper describes the implementation of the Tiny Eats GRU, a shallow GRU neural network, on a low power micro-controller, Arm Cortex M0+, to classify eating episodes. Tiny Eats GRU is a hybrid of the traditional GRU [2] and eGRU [3] to make it small and fast enough to fit on the Arm Cortex M0+ with comparable accuracy to the traditional GRU. The Tiny Eats GRU utilizes only 4% of the Arm Cortex M0+ memory and identifies eating or non-eating episodes with 6 ms latency and accuracy of 95.15%.
Four-Dimensional Dominance Range Reporting in Linear Space
Venue: arXiv cs.DS 2020
Year: 2020

In this paper we study the four-dimensional dominance range reporting problem and present data structures with linear or almost-linear space usage. Our results can be also used to answer four-dimensional queries that are bounded on five sides. The first data structure presented in this paper uses linear space and answers queries in $O(\log^{1+\varepsilon}n + k\log^{\varepsilon} n)$ time, where $k$ is the number of reported points, $n$ is the number of points in the data structure, and $\varepsilon$ is an arbitrarily small positive constant. Our second data structure uses $O(n \log^{\varepsilon} n)$ space and answers queries in $O(\log n+k)$ time. These are the first data structures for this problem that use linear (resp. $O(n\log^{\varepsilon} n)$) space and answer queries in poly-logarithmic time. For comparison the fastest previously known linear-space or $O(n\log^{\varepsilon} n)$-space data structure supports queries in $O(n^{\varepsilon} + k)$ time (Bentley and Mauer, 1980). Our results can be generalized to $d\ge 4$ dimensions. For example, we can answer $d$-dimensional dominance range reporting queries in $O(\log\log n (\log n/\log\log n)^{d-3} + k)$ time using $O(n\log^{d-4+\varepsilon}n)$ space. Compared to the fastest previously known result (Chan, 2013), our data structure reduces the space usage by $O(\log n)$ without increasing the query time.
New Complementary Sets with Low PAPR Property under Spectral Null Constraints
Venue: arXiv cs.IT 2020
Year: 2020

Complementary set sequences (CSSs) are useful for dealing with the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM) systems. In practical OFDM transmission, however, certain sub-carriers maybe reserved and/or prohibited to transmit signals, leading to the so-called \emph{spectral null constraint} (SNC) design problem. For example, the DC sub-carrier is reserved to avoid the offsets in D/A and A/D converter in the LTE systems. While most of the current research focus on the design of low PAPR CSSs to improve the code-rate, few works address the aforementioned SNC in their designs. This motivates us to investigate CSSs with SNC as well as low PAPR property. In this paper, we present systematic constructions of CSSs under SNCs and low PAPR. First, we show that mutually orthogonal complementary sets (MOCSs) can be used as \emph{seed sequences} to generate new CSSs with SNC and low PAPR, and then provide an iterative technique for the construction of MOCSs which can be further used to generate complementary sets (CSs) with low PAPRs and spectral nulls at \emph{varying} positions in the designed sequences. Next, inspired by a recent idea of Chen, we propose a novel construction of these \emph{seed} MOCSs with non-power-of-two lengths from generalized Boolean functions.
Performance Evaluation of Advanced Deep Learning Architectures for Offline Handwritten Character Recognition
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents a hand-written character recognition comparison and performance evaluation for robust and precise classification of different hand-written characters. The system utilizes advanced multilayer deep neural network by collecting features from raw pixel values. The hidden layers stack deep hierarchies of non-linear features since learning complex features from conventional neural networks is very challenging. Two state of the art deep learning architectures were used which includes Caffe AlexNet and GoogleNet models in NVIDIA DIGITS.The frameworks were trained and tested on two different datasets for incorporating diversity and complexity. One of them is the publicly available dataset i.e. Chars74K comprising of 7705 characters and has upper and lowercase English alphabets, along with numerical digits. While the other dataset created locally consists of 4320 characters. The local dataset consists of 62 classes and was created by 40 subjects. It also consists upper and lowercase English alphabets, along with numerical digits. The overall dataset is divided in the ratio of 80% for training and 20% for testing phase. The time required for training phase is approximately 90 minutes. For validation part, the results obtained were compared with the groundtruth. The accuracy level achieved with AlexNet was 77.77% and 88.89% with Google Net. The higher accuracy level of GoogleNet is due to its unique combination of inception modules, each including pooling, convolutions at various scales and concatenation procedures.